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        <title>Classification and Clustering of Arguments with Contextualized Word Embeddings</title>
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    <abstract>We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.</abstract>
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%0 Conference Proceedings
%T Classification and Clustering of Arguments with Contextualized Word Embeddings
%A Reimers, Nils
%A Schiller, Benjamin
%A Beck, Tilman
%A Daxenberger, Johannes
%A Stab, Christian
%A Gurevych, Iryna
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F reimers-etal-2019-classification
%X We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.
%R 10.18653/v1/P19-1054
%U https://aclanthology.org/P19-1054
%U https://doi.org/10.18653/v1/P19-1054
%P 567-578

Markdown (Informal)

[Classification and Clustering of Arguments with Contextualized Word Embeddings](https://aclanthology.org/P19-1054) (Reimers et al., ACL 2019)

• Classification and Clustering of Arguments with Contextualized Word Embeddings (Reimers et al., ACL 2019)

ACL

• Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019. Classification and Clustering of Arguments with Contextualized Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567–578, Florence, Italy. Association for Computational Linguistics.